In our work, we employ an edge-preserving filter, weighted least squares optimization framework (WLS) [1]}. The WLS is a non-linear method that captures image details at a variety of scales via multi-scale decompositions. The WLS helps to find an approximate enhanced image \(g^{Filtered}\)  that is close to the input image \(g\) , and also at the same time, is smooth along significant gradients, thus resulting to sharper preserved edges. Formally, it is defined as:
\(\hspace{42.67912pt}g^{Filtered} = F_{\lambda }(g) = (I + \lambda L_{g})^{-1}g\) 
